ArticleScientific reports2025
Dynamic interactions of COVID-19 incidences, mobility, and social distancing policies in seoul: A VAR model approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Complex network analysis of mobility dynamics in Seoul during the COVID-19 pandemic 2020-2022.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
COVID-19 incidence, mobility, and social distancing policies are interdependently linked, with the pathways and level of influence varying over time by region. Using weekly, district-specific time series data, we examined whether the effect and underlying pathways of social distancing policies on changes in COVID-19 incidence and mobility differ between commercial and non-commercial districts in Seoul. We employed the Vector Autoregression with Exogenous variables (VARX) model to analyze the interdependencies among COVID-19 incidences, mobility patterns, and social distancing policies over time from June 2020 to November 2021 across Seoul, including district-specific effects. The model incorporated vaccination coverage as an external variable to account for population-level immunity. Impulse Response Functions (IRFs) were used to interpret the results of the citywide VARX model, illustrating how changes in one variable influenced others over time. Social distancing policies had minimal direct effects on incidence rates but indirectly reduced them by decreasing mobility. Mobility observed two weeks prior demonstrated a positive correlation with current COVID-19 incidence, particularly in commercial districts, indicating that increased movement significantly contributes to transmission. On the other hand, rising COVID-19 cases led to decreased mobility, especially in non-commercial districts, likely driven by fear of infection and heightened self-restriction. Policies were more effective in reducing mobility within commercial districts, likely due to stricter enforcement and higher population density, which amplified compliance impacts. Reduced mobility in commercial districts following stricter policies indicates that targeted restrictions in high-traffic areas can effectively help control COVID-19 transmission in Seoul. In contrast, the more pronounced voluntary movement reductions in non-commercial areas, driven by increased incidence, suggest that enhanced regular testing could play a more significant role in disease control in these regions. While policies reduce mobility, their slower impact on incidence rates, at least a two-week time lag, highlights the need for proactive implementation to achieve measurable decreases in transmission.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.